GEOINT for Policing

The traditional who-what-when-where crime report is starting to acquire many more details—from the proximity of the nearest ATM or street light to the occupational, educational, or religious significance of the date.

These are the kind of data points and insights any cop on the scene would notice, but which could then easily get lost in the system.

By combining increasingly detailed databases with powerful software that can detect patterns almost as fast as reports are filed, police departments and other first responders can deploy their resources more efficiently, be more accountable to citizens, and perhaps even develop a sense of where crime is likely to occur next.

But there’s also a risk of confusion and unnecessary expense as busy police departments try to assess pitches from geospatial intelligence (GEOINT) firms.

“We’re almost getting flooded by them,” said Police Lt. Joseph Flynn, assistant commander of the Fairfax County Police Department’s Criminal Intelligence Division and deputy director of the Northern Virginia Regional Intelligence Center. “It’s still so new, and what do we want?”

Prescient Analytics

Applying GEOINT to policing begins with the basics of incident reports and 911 calls, explained Robert Cheetham, CEO of the Philadelphia firm Azavea. Its subsidiary HunchLab performs some of the leading work in next-generation policing software.

HunchLab models incorporate “a whole range of other things,” Cheetham said. He listed both nearby amenities and businesses—transit stops, ATMs, liquor stores, and even lighting—in addition to temporal factors such as the time of the day, the day of the week, whether school was in session, and whether it was a holiday.

In each municipality, HunchLab builds a model that incorporates these inputs and calculates the potential harm of types of crime using the RAND Corporation’s “Cost of Crime” calculations. The results—at an annual subscription cost of $20,000 to $80,000 depending on municipality size, with custom pricing for the largest cities—not only illuminate crime trends but offer a hint of where they’re likely to head.

“What we’re doing is not prediction,” Cheetham said. “It’s more of a forecast of a difference in risk.”

This 10-minute overview outlines how HunchLab’s proactive patrol management system helps police departments patrol the right places, understand risks across their jurisdiction, use effective tactics based on those risks, and track their crime and response time trends. (Video credit: Azavea)

The Chicago Police Department (CPD) ranks as HunchLab’s highest-profile client on account of the high rate of shootings across the city. CPD began deploying HunchLab’s system in January 2017; by mid-year, the department had brought it to the six of its 25 districts that account for 25 percent of the city’s shootings.

“We’ve seen what I’ll say are promising results,” said Jonathan Lewin, chief of CPD’s Bureau of Technical Services. In the first two districts to get this upgrade, shootings have so far dropped by 33 percent, well above the 14 percent drop citywide.

Lewin added the department is using the data it collects not just to dispatch officers faster but to speed actions by other parts of city government.

“One of the things we looked at was 311 calls for streetlights out,” he said. “Does that tend to correlate with nighttime shootings?”

As a result, Lewin said, the city is now prioritizing its deployment of connected LED streetlights “in some of the areas where we think it might have the greatest impact on reducing crime.”

However, if law enforcement agencies don’t clean up their data before implementing forecasting technologies, they risk being led astray.

“Not having the proper protocols and data governance policies to prevent incomplete and inaccurate data entry leads to the issue of ‘junk in, junk out,’” Jody Weis, public safety lead at Accenture, warned via e-mail. “The finest analytic system, with the absolute best algorithms, will be useless if the data it is analyzing isn’t accurate.”

Jeff Gallagher, a GIS specialist with the Fairfax County Police Department, advised cultivating relationships with local government information technology and GIS professionals.

“Get out of the little pigeonhole and see the amount of data your county has,” Gallagher said.

Unblinking Eyes

In addition to information derived from officers, citizens, and databases, many police departments also have unblinking eyes on their communities in the form of automated sensors that collect real-time data for quick analysis.

Data from sensors such as the ShotSpotter gunshot detection and location service can be integrated with other data into GIS systems for analysis by police departments. (Image credit: ShotSpotter)

Such data integration can add to a department’s budget and can encounter resistance from citizens. For instance, Lewin said CPD cameras got a better reception in communities after the department switched to a less obvious model that didn’t have continuously flashing blue lights.

But they do work.

“People are now actually catching criminals in the act based on the predictive analysis of all this historic and real-time data,” Beck said.

However, Beck continued, with the deluge of new information also comes the risk of overloading officers with data that should first pass an analyst’s eyes.

“We’re seeing a lot more real-time crime centers in the U.S. and beyond,” Beck said, complimenting CPD for setting up these centers in individual districts. That, however, should not come at the cost of taking officers off the street.

Lewin said CPD hired eight civilian analysts to embed in these centers. It also had representatives from HunchLab and security systems firm Genetec go on ride-alongs with officers to learn how to refine their user interfaces.

An existing set of analog sensors—as in, the eyes and ears of citizens—remains essential.

“Don’t become so over-reliant on [technology] that you become disconnected from the community,” said Sean Whitcomb, a sergeant and spokesman with the Seattle Police Department (SPD). He pointed to SPD’s regular incorporation of citizen input into its SeaStat crime-statistics program. “The value is increased exponentially because we supplement our own data with real-time feedback from the community.”

A Balancing Act

Collecting new data and building predictive models can also help police agencies increase their accountability to citizens.

“When I was a cop, we didn’t share any information with the public,” Beck said. “Now, police are sharing information about all of their activity, including use of force and police-involved shootings, and making that data open to the public.”

He pointed to the Philadelphia Police Department, whose website documents officer-involved shootings and allows visitors to compare the locations of those incidents with the locations of gun crimes across the city.

Public desire for accountability is another factor driving law enforcement agencies to deploy GEOINT.

In Chicago, the city’s Independent Police Review Authority now maintains a searchable use-of-force database, including audio and video from officers’ body cameras. And in Seattle, a 2011 Department of Justice investigation that found fault with SPD’s collection of data led the department to partner with Accenture to build a data analytics platform.

But data collection in policing can also generate public dissatisfaction with police departments. In 2016, citizens were angered to learn SPD had purchased Geofeedia’s social media analysis software two years earlier.

Weis and Beck each pointed to social media monitoring as the next frontier in the use of GEOINT by police. But after SPD’s attempts to glean intelligence from status updates went awry, the resulting blowback led Facebook and Twitter to yank Geofeedia’s access to their networks.

“There’s a very fine line between government surveillance and spying,” SPD’s Whitcomb said, adding the department now focuses on the social postings of individual suspects. “Something causes more harm than good if it erodes public trust and confidence.”

Said CPD’s Lewin, “Community partnership requires that we engage our stakeholders, and part of that is being as transparent as possible.”

Jay Stanley, senior policy analyst for the American Civil Liberties Union, emphasized police departments and the GEOINT industry should maintain transparency to help “reduce bias and improve trust with communities.”

Cheetham echoed Stanley’s point.

“I want to be on the right side of history on this,” he said.

More Research Needed

Cheetham and Stanley separately noted the need for more published research on the effectiveness of GEOINT and predictive policing.

For example, while the Police Executive Research Forum has spent years investigating law enforcement best practices, it has yet to study this technology, Director of Communications Craig Fischer wrote via e-mail.

Lewin said CPD is now working with the University of Chicago’s Crime Lab to research how its initial deployment of predictive policing technology has fared.

But, he added, the real-world consequences of police work make it difficult to run a classic experiment in which a control group is left out of a technological advance: “If you have something that could be effective, you want to use it.”